We are developing a method and system, the Sustained Attention Meter (SAM), for assessing the level and the quality of alertness in people with sleep disorders. The SAM uses multivariate pattern classification methods applied to physiological and behavioral data to make such assessments. It builds on past research indicating that spectral features of the task-related EEG are sensitive and reliable measures of sustained focused attention, and that physiological measures are highly sensitive to variations in alertness. In Phase I we implemented and evaluated a prototype portable data acquisition and testing platform, used it to record from patients with. sleep disorders, and determined that such patients display task-related modulation of the EEG similar to that observed in healthy control subjects. We also demonstrated that EEG pattern recognition methods could be used to derive indices that are highly sensitive to variations in alertness, and we further developed an automated testing and analysis system infrastructure. In Phase II we will fully implement an automated system that has been optimized for this application area, evaluate its efficacy in studies of patients with sleep disorders and of sleep-deprived normal volunteers, field test it in other laboratories and clinics, and prepare it for commercialization .